The term scan time, as used in the context of Magnetic Resonance Imaging (MRI), refers to the time required to acquire the k-space data needed to produce an image. In many MRI applications, it is beneficial to minimize scan time. For example, if the scan time can be reduced enough, images can be acquired in a single breath-hold which, in turn, will reduce effects such as motion blurring in the acquired image. Additionally, reducing the scan time increases the overall efficiency of the clinician, as well as the scanning device itself. That is, if each scan can be performed faster, then more patients can be scanned by the clinician in a given amount of time.
One approach to reducing the scan time is to undersample the k-space below the Nyquist sampling rate. If fewer lines are acquired during each scan, the overall time required to complete a scan can be proportionally reduced. In order to reduce the total number of lines acquired during each scan, lines are skipped. The number of lines skipped during each scan is characterized by a MRI device setting referred to as the acceleration factor. For example, if the acceleration factor is set to be 2, every other line of k-space data will be acquired. Similarly, if the acceleration factor is 4, every fourth line will be acquired. Although increasing the acceleration factor is generally beneficial in reducing scan time, it results in missing lines of data which must be calculated via interpolation or some other k-space fitting strategy.
Generalized Auto-calibrating Partially Parallel Acquisitions (GRAPPA) is a parallel imaging reconstruction technique that addresses the missing lines of data resulting from increasing the acceleration factor. However, GRAPPA, like any data fitting process, is dependent spacing of the data points used to fit the data. GRAPPA achieves very good image quality when the acceleration factor is set to a low value such as 2. However, in the case of high acceleration factor, image artifacts can be quite visible. More specifically, as the acceleration factor increases, the k-space data becomes increasingly undersampled and more values need to be interpolated with less data. As a result, the overall interpolation error associated with applying the fitting strategy increases. Thus, there is a need for improvements to fitting strategies such as GRAPPA to support high acceleration factors.